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The use of functional brain imaging for research and diagnosis has benefitted greatly from the recent advancements in neuroimaging technologies, as well as the explosive growth in size and availability of fMRI data. While it has been shown in literature that using multiple and large scale fMRI datasets can improve reproducibility and lead to new discoveries, the computational and informatics systems...
Although there is growing recognition and interest in the dynamic organization of the brain's functional architecture, the characterization and systematic analysis of such dynamic patterns are still largely under-investigated. In this work, we proposed a novel multi-scale dynamic dictionary learning framework which decomposes the input fMRI signals into functional networks via sliding/growing time...
Natural stimulus fMRI has been increasingly used in the brain imaging and brain mapping fields thanks to its more realistic stimulation of the brain's perceptive and cognitive systems. However, identifying consistent functional networks across different brains in natural stimulus fMRI data has been challenging due to the intrinsic variability of individual brain's responses and a variety of sources...
Multiple recent neuroimaging studies revealed that functional interactions within brain regions are locally clustered into small sub-networks where different dynamics of functional interaction occur. However, integration models investigating such functional brain dynamics have been rarely explored. In this paper, a novel Bayesian inference model is developed to partition the brain regions into different...
For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project high-quality fMRI data, we show that a number of reproducible and...
Dynamic functional interaction has received much attention recently in the field of neuroimaging. Past studies reveal that the dynamics of functional interactions only exists in part of brain. In this paper, a novel Bayesian inference model is developed to bi-partition the brain regions into dynamic/stable sub networks and to simultaneously segment the temporal sequence of dynamic network into several...
In the human brain mapping field, virtually most existing fMRI activation detection methods, such as the general linear model (GLM), have assumed that the fMRI signal magnitude should follow the alternations of baseline and task periods. However, our extensive observation shows that different brain regions or networks exhibit quite dissimilar temporal activation patterns. Inspired by this observation,...
Multiple recent neuroimaging studies have demonstrated that the human brain's function undergoes remarkable temporal dynamics. However, quantitative characterization and modeling of such functional dynamics have been rarely explored. To fill this gap, we presents a novel Bayesian connectivity change point model (BCCPM), to analyze the joint probabilities among the nodes of brain networks between different...
Based on the structural connectomes constructed from diffusion tensor imaging (DTI) data, we present a novel framework to discover functional connectomics signatures from resting-state fMRI (R-fMRI) data for the characterization of brain conditions. First, by applying a sliding time window approach, the brain states represented by functional connectomes were automatically divided into temporal quasi-stable...
Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric, neurodevelopmental and neurobehavioral disorders occurring in the childhood of human. The typical symptoms are characterized as excessive inattention, hyperactivity/impulsiveness or their combination. Traditionally, it has been thought to be a partial dysfunction caused by prefrontal-striatal circuits. Recent studies,...
Structural and functional brain connectivity has been extensively studied via diffusion tensor imaging (DTI) and functional MRI (fMRI) in recent years. An important aspect that has not been adequately addressed before is the connectivity state change in structurally-connected brain regions. In this paper, we present an intuitive approach that extracts feature vectors describing the functional connectivity...
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